Infrawise

Your AI coding assistant finally knows your infra.

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Infrawise gives AI coding assistants deterministic infrastructure awareness.

It statically analyzes your codebase, cloud infrastructure, and database schemas, then exposes that context through MCP so tools like Claude Code can understand your actual tables, indexes, query patterns, and service relationships instead of guessing from source files alone.

infrawise start --claude, then Claude Code answers an SQS handler question with the exact event shape and queue risks pulled live from infrawise

Why this exists

New software developers don't write wrong code. Claude Code writes wrong code and they ship it. Infrawise is the only thing standing between Claude Code's generated output and a production incident.

AI coding assistants can read your source files but have no deterministic knowledge of your infrastructure. They do not know which GSIs exist, how tables are partitioned, which functions already trigger scans, or where indexes are missing. So they guess.

Infrawise replaces guessing with infrastructure-aware context.

Without Infrawise, an AI assistant might:

• Suggest a .scan() on your Orders table that has 50M rows
• Recommend adding a GSI on status that you already have
• Write a SELECT * when you need to keep query cost low
• Not notice that 5 functions are already hammering the same partition key

With Infrawise, it knows:

• Your exact table schemas, partition keys, sort keys, and GSIs
• Which functions query which tables and how
• Which patterns are already flagged as high severity
• The exact CREATE INDEX SQL or GSI config for your tables — not generic advice

What Infrawise is not

Infrawise is not an AI agent framework, an infrastructure provisioning tool, an observability platform, or a cloud management dashboard.

It is a deterministic infrastructure intelligence layer for AI-assisted development.

Installation

Requires Node.js 22 or later (node --version).

npm install -g infrawise

or use without installing:

npx infrawise start --claude

Quick start

cd your-project
infrawise start --claude

That's it. Infrawise will:

1. Probe your environment and generate infrawise.yaml (first time only — asks which AWS profile to use only if you have several)
2. Scan your AWS services, databases, and codebase
3. Write .mcp.json so your editor auto-connects on every future launch
4. Open Claude Code with all 22 MCP tools ready

Every time after:

claude    # no infrawise command needed — editor manages the connection

Analysis is cached for 24 hours. When the cache is stale, infrawise serve --stdio (spawned automatically by your editor) refreshes it at session start. File changes are detected within the session and the code graph is updated automatically.

Findings (3 total)

1. [HIGH] Full table scan detected on DynamoDB table "Orders"
   listAllOrders() scans without any filter — reads every item in the table.
   Recommendation: Replace Scan with Query using a partition key or add a GSI.

2. [MEDIUM] PostgreSQL table "users" has no index on column "email"
   Filtering on "email" causes sequential scans.
   Recommendation: CREATE INDEX CONCURRENTLY idx_users_email ON users(email);

3. [MEDIUM] DynamoDB table "Sessions" accessed by 6 distinct code paths
   High access concentration may create hot partition issues at scale.

Using with AI coding assistants

Claude Code (recommended)

infrawise start --claude

Writes .mcp.json to your project root (merging with any MCP servers already configured there) and opens Claude Code. Claude Code reads .mcp.json automatically on every launch and manages the infrawise serve --stdio process — no server to start, no ports to configure.

Cursor

infrawise start --cursor

Writes .cursor/mcp.json (merging with any existing MCP servers) and opens Cursor. All 22 infrawise tools are available in Cursor's MCP panel.

VS Code

infrawise start --vscode

Writes .vscode/mcp.json (merging with any existing MCP servers) and opens VS Code. The tools are available to Copilot agent mode via the MCP servers panel.

Any editor (no flag)

infrawise start

Writes .mcp.json (merging with any existing MCP servers) and exits. Open whichever editor you prefer — point it at infrawise serve --stdio --config /path/to/infrawise.yaml as an MCP server command.

HTTP transport (alternative)

If your editor or workflow requires an HTTP MCP endpoint instead of stdio:

infrawise serve    # starts server at http://localhost:3000/mcp

Add to your editor's MCP config:

{
  "mcpServers": {
    "infrawise": {
      "url": "http://localhost:3000/mcp"
    }
  }
}

MCP tools

Every response carries a dataHealth block with a fixed shape: when the infrastructure was read and how long ago, the status of each source behind that answer, whether cdk.out has been synthed since, and the command that refreshes. Every key is always present, so nothing has to be inferred from a field's absence — an empty result you can't distinguish from a failed one reads as "no queues need a DLQ" when the truth is "SQS was never listed".

Infrawise reports; it doesn't rule. Pass maxAgeSeconds when a question is point-in-time and the answer tells you whether the data meets it (advisory — the data still comes back, marked). A running server rechecks the cache on each tool call, so an open session picks up a fresh infrawise analyze on its next question, without a restart. infrawise analyze and infrawise check print the same source warnings and stop calling a run clean when any source went unread.

Age is a proxy for drift, not drift itself — a three-day-old snapshot of an untouched account is accurate, and a five-minute-old one taken before a terraform apply isn't. How Infrawise handles staleness covers where that proxy misleads and what to do about it; the data freshness reference is the field-by-field table and the freshness config key.

CLI reference

infrawise analyze options

# Export a shareable findings report
infrawise analyze --output report.md

# Only show high-severity issues
infrawise analyze --severity high

# High-severity issues only, saved to a file
infrawise analyze --severity high --output report.md

infrawise check options (CI/CD)

check runs a fresh analysis and sets a non-zero exit code when blocking findings exist, so it can gate a pipeline without an AI editor.

# Block a deploy if any high-severity finding exists (exit 1)
infrawise check

# Stricter gate — fail on medium and above
infrawise check --fail-on medium

infrawise serve options

Configuration

infrawise.yaml is generated by infrawise start (or infrawise start --interactive for the guided wizard) and lives in your repo root. Every service must be explicitly enabled: true — infrawise never connects to anything not listed in config.

Connection strings support ${ENV_VAR} substitution so passwords never need to be committed:

postgres:
  enabled: true
  connectionString: postgresql://infrawise_ro:${DB_PASSWORD}@host:5432/mydb

Full example:

project: payments-service

aws:
  profile: default # AWS profile from ~/.aws/credentials
  region: ap-south-1

dynamodb:
  enabled: true
  includeTables: # omit to include all tables
    - Orders
    - Users

postgres:
  enabled: true
  connectionString: postgresql://infrawise_ro:${DB_PASSWORD}@host:5432/mydb

mysql:
  enabled: false
  connectionString: ''

mongodb:
  enabled: false
  connectionString: ''

sqs:
  enabled: true

sns:
  enabled: true

ssm:
  enabled: true
  paths: [] # filter by prefix e.g. ["/myapp/prod"]

secretsManager:
  enabled: true

lambda:
  enabled: true
  includeFunctions: # omit to include all functions
    - myFunction
    - anotherFunction

eventbridge:
  enabled: true

rds:
  enabled: false

s3:
  enabled: false

apiGateway:
  enabled: false

cognito:
  enabled: false

kinesis:
  enabled: false

msk:
  enabled: false

elasticache:
  enabled: false

cloudfront:
  enabled: false

runtimeSignals:
  enabled: false # Lambda throttles/errors + queue age via CloudWatch metrics
  windowHours: 24

cloudwatchLogs:
  enabled: false
  logGroupPrefixes: []
  windowHours: 24

analysis:
  hotPartitionThreshold: 5
  hotPartitionThresholds:
    high-traffic-table: 12

freshness:
  suggestRefreshAfterHours: 6 # when MCP responses start hinting to re-analyze

AWS setup

Infrawise is read-only. Minimum IAM policy for DynamoDB:

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": ["dynamodb:ListTables", "dynamodb:DescribeTable"],
      "Resource": "*"
    }
  ]
}

For the full policy across all supported services, how to scope it to only the services you enable, and using a session policy for temporary scoped credentials, see the AWS setup guide.

For SSO profiles, log in before running infrawise:

aws sso login --profile myprofile

PostgreSQL setup (optional)

Create a read-only user for infrawise:

CREATE USER infrawise_ro WITH PASSWORD 'yourpassword';
GRANT CONNECT ON DATABASE yourdb TO infrawise_ro;
GRANT USAGE ON SCHEMA public TO infrawise_ro;
GRANT SELECT ON ALL TABLES IN SCHEMA public TO infrawise_ro;

For Amazon RDS: allow inbound on port 5432 from your machine's IP in the security group.

Analysis capabilities

Infrawise has two analysis layers:

Infrastructure analysis (all languages)

Works from AWS APIs, database schema introspection, and IaC files — no dependency on application code:

Code correlation analysis (TypeScript / JavaScript / Python)

Uses ts-morph AST analysis to detect which functions call which tables and how:

Python repositories are scanned with a bundled stdlib-ast scanner (requires python3 on PATH; skipped with a warning otherwise): boto3 clients and dynamodb.Table() resources, cursor.execute SQL, pymongo collections, and kafka-python/confluent-kafka producers and consumers. Language detection is automatic — TypeScript and Python scans each run only when matching files exist.

Projects in other languages still get full value from infrastructure-level analyzers — code correlation (function-to-table mapping, N+1 patterns) currently supports TypeScript, JavaScript, and Python.

The scanner supports: AWS SDK v3/v2 for DynamoDB, pg/Prisma/Knex for PostgreSQL, mysql2/Knex for MySQL, driver/Mongoose for MongoDB, AWS SDK v3 for SQS/SNS/SSM/Secrets/Lambda, and kafkajs for Kafka topics (producer/consumer).

How it works

1. Infrawise scans your repository and infrastructure metadata
2. A graph engine maps services, schemas, indexes, and query patterns
3. Rule-based analyzers detect infrastructure and query anti-patterns
4. The resulting context is exposed through MCP
5. AI coding assistants query this context while generating code

Deterministic analysis

Infrawise does not use an LLM to analyze your infrastructure. All extraction and analysis are deterministic: AST parsing, schema introspection, rule-based analyzers, and graph correlation. LLMs are only consumers of the generated context through MCP.

Security

• Read-only — never writes to AWS or your database, never executes DDL
• Local-first — everything runs on your machine, nothing sent to external servers
• No telemetry — zero data collection
• Credentials — uses your existing AWS credential chain, never stored by infrawise

🔒 Security & Project Naming Note

You might see this package flagged on certain supply-chain security scanners under "deceptive naming." This is a false positive triggered by automated tools because of the prefix "infra." This project is completely safe, independent, and unaffiliated with any commercial trademarks.

Architecture overview

Source layout

src/
  types.ts      Shared type definitions
  core/         Config (Zod + YAML), logger (Pino), local cache
  graph/        Graph engine — nodes, edges, builder
  adapters/
    aws/        DynamoDB, S3, Lambda, SQS/SNS/SSM/Secrets/EventBridge/RDS/APIGateway, CloudWatch
    db/         PostgreSQL, MySQL, MongoDB
    iac/        Terraform, CDK, CloudFormation (local file parsing)
  analyzers/    39 rule-based analyzers
  context/      Repository scanner (ts-morph AST + Python stdlib-ast subprocess)
  server/       Fastify MCP server (@modelcontextprotocol/sdk, Streamable HTTP)
  cli/          CLI commands (Commander.js)

Current limitations

• Code-level correlation supports TypeScript, JavaScript, and Python (Python requires python3 on PATH)
• Dynamically constructed queries may not always be resolved statically
• Runtime tracing is not yet implemented
• Large monorepos may require future incremental analysis optimization

Roadmap

Feature roadmap is tracked in the Infrawise v1 project board. Feature requests and upvotes welcome.

Demo

Two demos run infrawise against real AWS APIs emulated locally in Docker, at zero cost and with no real AWS account.

• demo/floci/ uses Floci, an MIT-licensed emulator that covers every service infrawise supports — including CloudFront, API Gateway v2, RDS, Cognito, Kinesis, ElastiCache, and MSK. No auth token, no sign-up. Start here.
• demo/localstack/ uses LocalStack community edition, which covers the core services.

Both listen on port 4566, so run one at a time.

Contributing

See CONTRIBUTING.md for a full walkthrough — including how to add a new service adapter, a new analyzer, and the PR checklist.

Releasing

Before releasing, run pnpm check:docs — it fails if the version or the MCP tool list in README.md/AGENTS.md/llms.txt drifted from src/server/index.ts.

pnpm release patch    # 0.1.2 → 0.1.3  (bug fixes)
pnpm release minor    # 0.1.2 → 0.2.0  (new features, backwards compatible)
pnpm release major    # 0.1.2 → 1.0.0  (breaking changes)
pnpm release 1.5.0    # explicit version

Bumps package.json, commits, tags, pushes, and creates a draft GitHub release with notes from commit messages. Then publish the draft on GitHub to trigger npm publish.

License

MIT